Spatial Model of Dengue Hemorrhagic Fever (DHF) Risk: Scoping Review

Abstract

Abstract This review aims to provide a comprehensive overview of the important predictors, and additionally spatial modeling tools capable of producing Dengue Hemorrhagic Fever (DHF) risk maps. A literature search was conducted in PubMed, Scopus, Science Direct, and Google Scholar for studies reporting DHF risk factors. The Preferred Reporting Items for Systematic Reviews (PRISMA) 2020 statement is used to report this scoping review. It lasted from January 2011 to August of 2022. Initially 1329 articles were found, after inclusion and exclusion criteria, 45 manuscripts were selected. A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and Machine Learning technique. We found that There was no pattern of predictor use associated with particular approaches; instead, a wide range of predictors was used to create DHF risk maps. Predictors are various variables or factors that are considered when assessing the likelihood or intensity of DHF outbreaks in a specific area in the context of DHF risk mapping. These predictors can include climatology factors (e.g., temperature, rainfall, humidity), socio-economic indicators (e.g., population density, urbanization level), environmental factors (land-use, elevation) and other relevant factors (e.g., mosquito abundance, previous DHF cases). The spatial model of DHF risk is a valuable tool for public health authorities, policymakers, and communities to identify areas at higher risk of dengue transmission, but its limitations underscore the importance of complementing it with other approaches and considering contextual factors for a more holistic assessment of DHF outbreaks. It enables targeted interventions, such as vector control measures and public awareness campaigns, to be implemented in high-risk areas, ultimately helping to mitigate the impact of dengue outbreaks and protect public health.